id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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1210.4875 | A Theory of Goal-Oriented MDPs with Dead Ends | cs.AI | Stochastic Shortest Path (SSP) MDPs is a problem class widely studied in AI,
especially in probabilistic planning. They describe a wide range of scenarios
but make the restrictive assumption that the goal is reachable from any state,
i.e., that dead-end states do not exist. Because of this, SSPs are unable to
model various scenarios that may have catastrophic events (e.g., an airplane
possibly crashing if it flies into a storm). Even though MDP algorithms have
been used for solving problems with dead ends, a principled theory of SSP
extensions that would allow dead ends, including theoretically sound algorithms
for solving such MDPs, has been lacking. In this paper, we propose three new
MDP classes that admit dead ends under increasingly weaker assumptions. We
present Value Iteration-based as well as the more efficient heuristic search
algorithms for optimally solving each class, and explore theoretical
relationships between these classes. We also conduct a preliminary empirical
study comparing the performance of our algorithms on different MDP classes,
especially on scenarios with unavoidable dead ends.
|
1210.4876 | Active Imitation Learning via Reduction to I.I.D. Active Learning | cs.LG stat.ML | In standard passive imitation learning, the goal is to learn a target policy
by passively observing full execution trajectories of it. Unfortunately,
generating such trajectories can require substantial expert effort and be
impractical in some cases. In this paper, we consider active imitation learning
with the goal of reducing this effort by querying the expert about the desired
action at individual states, which are selected based on answers to past
queries and the learner's interactions with an environment simulator. We
introduce a new approach based on reducing active imitation learning to i.i.d.
active learning, which can leverage progress in the i.i.d. setting. Our first
contribution, is to analyze reductions for both non-stationary and stationary
policies, showing that the label complexity (number of queries) of active
imitation learning can be substantially less than passive learning. Our second
contribution, is to introduce a practical algorithm inspired by the reductions,
which is shown to be highly effective in four test domains compared to a number
of alternatives.
|
1210.4877 | Incentive Decision Processes | cs.GT cs.MA | We consider Incentive Decision Processes, where a principal seeks to reduce
its costs due to another agent's behavior, by offering incentives to the agent
for alternate behavior. We focus on the case where a principal interacts with a
greedy agent whose preferences are hidden and static. Though IDPs can be
directly modeled as partially observable Markov decision processes (POMDP), we
show that it is possible to directly reduce or approximate the IDP as a
polynomially-sized MDP: when this representation is approximate, we prove the
resulting policy is boundedly-optimal for the original IDP. Our empirical
simulations demonstrate the performance benefit of our algorithms over simpler
approaches, and also demonstrate that our approximate representation results in
a significantly faster algorithm whose performance is extremely close to the
optimal policy for the original IDP.
|
1210.4878 | Join-graph based cost-shifting schemes | cs.AI | We develop several algorithms taking advantage of two common approaches for
bounding MPE queries in graphical models: minibucket elimination and
message-passing updates for linear programming relaxations. Both methods are
quite similar, and offer useful perspectives for the other; our hybrid
approaches attempt to balance the advantages of each. We demonstrate the power
of our hybrid algorithms through extensive empirical evaluation. Most notably,
a Branch and Bound search guided by the heuristic function calculated by one of
our new algorithms has recently won first place in the PASCAL2 inference
challenge.
|
1210.4879 | Causal Discovery of Linear Cyclic Models from Multiple Experimental Data
Sets with Overlapping Variables | stat.ME cs.AI stat.ML | Much of scientific data is collected as randomized experiments intervening on
some and observing other variables of interest. Quite often, a given phenomenon
is investigated in several studies, and different sets of variables are
involved in each study. In this article we consider the problem of integrating
such knowledge, inferring as much as possible concerning the underlying causal
structure with respect to the union of observed variables from such
experimental or passive observational overlapping data sets. We do not assume
acyclicity or joint causal sufficiency of the underlying data generating model,
but we do restrict the causal relationships to be linear and use only second
order statistics of the data. We derive conditions for full model
identifiability in the most generic case, and provide novel techniques for
incorporating an assumption of faithfulness to aid in inference. In each case
we seek to establish what is and what is not determined by the data at hand.
|
1210.4880 | Inferring Strategies from Limited Reconnaissance in Real-time Strategy
Games | cs.AI cs.GT cs.LG | In typical real-time strategy (RTS) games, enemy units are visible only when
they are within sight range of a friendly unit. Knowledge of an opponent's
disposition is limited to what can be observed through scouting. Information is
costly, since units dedicated to scouting are unavailable for other purposes,
and the enemy will resist scouting attempts. It is important to infer as much
as possible about the opponent's current and future strategy from the available
observations. We present a dynamic Bayes net model of strategies in the RTS
game Starcraft that combines a generative model of how strategies relate to
observable quantities with a principled framework for incorporating evidence
gained via scouting. We demonstrate the model's ability to infer unobserved
aspects of the game from realistic observations.
|
1210.4881 | Tightening Fractional Covering Upper Bounds on the Partition Function
for High-Order Region Graphs | cs.LG stat.ML | In this paper we present a new approach for tightening upper bounds on the
partition function. Our upper bounds are based on fractional covering bounds on
the entropy function, and result in a concave program to compute these bounds
and a convex program to tighten them. To solve these programs effectively for
general region graphs we utilize the entropy barrier method, thus decomposing
the original programs by their dual programs and solve them with dual block
optimization scheme. The entropy barrier method provides an elegant framework
to generalize the message-passing scheme to high-order region graph, as well as
to solve the block dual steps in closed-form. This is a key for computational
relevancy for large problems with thousands of regions.
|
1210.4882 | A Maximum Likelihood Approach For Selecting Sets of Alternatives | cs.AI | We consider the problem of selecting a subset of alternatives given noisy
evaluations of the relative strength of different alternatives. We wish to
select a k-subset (for a given k) that provides a maximum likelihood estimate
for one of several objectives, e.g., containing the strongest alternative.
Although this problem is NP-hard, we show that when the noise level is
sufficiently high, intuitive methods provide the optimal solution. We thus
generalize classical results about singling out one alternative and identifying
the hidden ranking of alternatives by strength. Extensive experiments show that
our methods perform well in practical settings.
|
1210.4883 | A Model-Based Approach to Rounding in Spectral Clustering | cs.LG cs.NA stat.ML | In spectral clustering, one defines a similarity matrix for a collection of
data points, transforms the matrix to get the Laplacian matrix, finds the
eigenvectors of the Laplacian matrix, and obtains a partition of the data using
the leading eigenvectors. The last step is sometimes referred to as rounding,
where one needs to decide how many leading eigenvectors to use, to determine
the number of clusters, and to partition the data points. In this paper, we
propose a novel method for rounding. The method differs from previous methods
in three ways. First, we relax the assumption that the number of clusters
equals the number of eigenvectors used. Second, when deciding the number of
leading eigenvectors to use, we not only rely on information contained in the
leading eigenvectors themselves, but also use subsequent eigenvectors. Third,
our method is model-based and solves all the three subproblems of rounding
using a class of graphical models called latent tree models. We evaluate our
method on both synthetic and real-world data. The results show that our method
works correctly in the ideal case where between-clusters similarity is 0, and
degrades gracefully as one moves away from the ideal case.
|
1210.4884 | A Spectral Algorithm for Latent Junction Trees | cs.LG stat.ML | Latent variable models are an elegant framework for capturing rich
probabilistic dependencies in many applications. However, current approaches
typically parametrize these models using conditional probability tables, and
learning relies predominantly on local search heuristics such as Expectation
Maximization. Using tensor algebra, we propose an alternative parameterization
of latent variable models (where the model structures are junction trees) that
still allows for computation of marginals among observed variables. While this
novel representation leads to a moderate increase in the number of parameters
for junction trees of low treewidth, it lets us design a local-minimum-free
algorithm for learning this parameterization. The main computation of the
algorithm involves only tensor operations and SVDs which can be orders of
magnitude faster than EM algorithms for large datasets. To our knowledge, this
is the first provably consistent parameter learning technique for a large class
of low-treewidth latent graphical models beyond trees. We demonstrate the
advantages of our method on synthetic and real datasets.
|
1210.4885 | A Case Study in Complexity Estimation: Towards Parallel Branch-and-Bound
over Graphical Models | cs.AI | We study the problem of complexity estimation in the context of parallelizing
an advanced Branch and Bound-type algorithm over graphical models. The
algorithm's pruning power makes load balancing, one crucial element of every
distributed system, very challenging. We propose using a statistical regression
model to identify and tackle disproportionally complex parallel subproblems,
the cause of load imbalance, ahead of time. The proposed model is evaluated and
analyzed on various levels and shown to yield robust predictions. We then
demonstrate its effectiveness for load balancing in practice.
|
1210.4886 | Exploiting Structure in Cooperative Bayesian Games | cs.GT cs.AI | Cooperative Bayesian games (BGs) can model decision-making problems for teams
of agents under imperfect information, but require space and computation time
that is exponential in the number of agents. While agent independence has been
used to mitigate these problems in perfect information settings, we propose a
novel approach for BGs based on the observation that BGs additionally possess a
different types of structure, which we call type independence. We propose a
factor graph representation that captures both forms of independence and
present a theoretical analysis showing that non-serial dynamic programming
cannot effectively exploit type independence, while Max-Sum can. Experimental
results demonstrate that our approach can tackle cooperative Bayesian games of
unprecedented size.
|
1210.4887 | Hilbert Space Embeddings of POMDPs | cs.LG cs.AI stat.ML | A nonparametric approach for policy learning for POMDPs is proposed. The
approach represents distributions over the states, observations, and actions as
embeddings in feature spaces, which are reproducing kernel Hilbert spaces.
Distributions over states given the observations are obtained by applying the
kernel Bayes' rule to these distribution embeddings. Policies and value
functions are defined on the feature space over states, which leads to a
feature space expression for the Bellman equation. Value iteration may then be
used to estimate the optimal value function and associated policy. Experimental
results confirm that the correct policy is learned using the feature space
representation.
|
1210.4888 | Local Structure Discovery in Bayesian Networks | cs.LG cs.AI stat.ML | Learning a Bayesian network structure from data is an NP-hard problem and
thus exact algorithms are feasible only for small data sets. Therefore, network
structures for larger networks are usually learned with various heuristics.
Another approach to scaling up the structure learning is local learning. In
local learning, the modeler has one or more target variables that are of
special interest; he wants to learn the structure near the target variables and
is not interested in the rest of the variables. In this paper, we present a
score-based local learning algorithm called SLL. We conjecture that our
algorithm is theoretically sound in the sense that it is optimal in the limit
of large sample size. Empirical results suggest that SLL is competitive when
compared to the constraint-based HITON algorithm. We also study the prospects
of constructing the network structure for the whole node set based on local
results by presenting two algorithms and comparing them to several heuristics.
|
1210.4889 | Learning STRIPS Operators from Noisy and Incomplete Observations | cs.LG cs.AI stat.ML | Agents learning to act autonomously in real-world domains must acquire a
model of the dynamics of the domain in which they operate. Learning domain
dynamics can be challenging, especially where an agent only has partial access
to the world state, and/or noisy external sensors. Even in standard STRIPS
domains, existing approaches cannot learn from noisy, incomplete observations
typical of real-world domains. We propose a method which learns STRIPS action
models in such domains, by decomposing the problem into first learning a
transition function between states in the form of a set of classifiers, and
then deriving explicit STRIPS rules from the classifiers' parameters. We
evaluate our approach on simulated standard planning domains from the
International Planning Competition, and show that it learns useful domain
descriptions from noisy, incomplete observations.
|
1210.4890 | The Complexity of Approximately Solving Influence Diagrams | cs.AI | Influence diagrams allow for intuitive and yet precise description of complex
situations involving decision making under uncertainty. Unfortunately, most of
the problems described by influence diagrams are hard to solve. In this paper
we discuss the complexity of approximately solving influence diagrams. We do
not assume no-forgetting or regularity, which makes the class of problems we
address very broad. Remarkably, we show that when both the tree-width and the
cardinality of the variables are bounded the problem admits a fully
polynomial-time approximation scheme.
|
1210.4891 | Hokusai - Sketching Streams in Real Time | cs.DB cs.DS | We describe Hokusai, a real time system which is able to capture frequency
information for streams of arbitrary sequences of symbols. The algorithm uses
the CountMin sketch as its basis and exploits the fact that sketching is
linear. It provides real time statistics of arbitrary events, e.g. streams of
queries as a function of time. We use a factorizing approximation to provide
point estimates at arbitrary (time, item) combinations. Queries can be answered
in constant time.
|
1210.4892 | Unsupervised Joint Alignment and Clustering using Bayesian
Nonparametrics | cs.LG stat.ML | Joint alignment of a collection of functions is the process of independently
transforming the functions so that they appear more similar to each other.
Typically, such unsupervised alignment algorithms fail when presented with
complex data sets arising from multiple modalities or make restrictive
assumptions about the form of the functions or transformations, limiting their
generality. We present a transformed Bayesian infinite mixture model that can
simultaneously align and cluster a data set. Our model and associated learning
scheme offer two key advantages: the optimal number of clusters is determined
in a data-driven fashion through the use of a Dirichlet process prior, and it
can accommodate any transformation function parameterized by a continuous
parameter vector. As a result, it is applicable to a wide range of data types,
and transformation functions. We present positive results on synthetic
two-dimensional data, on a set of one-dimensional curves, and on various image
data sets, showing large improvements over previous work. We discuss several
variations of the model and conclude with directions for future work.
|
1210.4893 | Sparse Q-learning with Mirror Descent | cs.LG stat.ML | This paper explores a new framework for reinforcement learning based on
online convex optimization, in particular mirror descent and related
algorithms. Mirror descent can be viewed as an enhanced gradient method,
particularly suited to minimization of convex functions in highdimensional
spaces. Unlike traditional gradient methods, mirror descent undertakes gradient
updates of weights in both the dual space and primal space, which are linked
together using a Legendre transform. Mirror descent can be viewed as a proximal
algorithm where the distance generating function used is a Bregman divergence.
A new class of proximal-gradient based temporal-difference (TD) methods are
presented based on different Bregman divergences, which are more powerful than
regular TD learning. Examples of Bregman divergences that are studied include
p-norm functions, and Mahalanobis distance based on the covariance of sample
gradients. A new family of sparse mirror-descent reinforcement learning methods
are proposed, which are able to find sparse fixed points of an l1-regularized
Bellman equation at significantly less computational cost than previous methods
based on second-order matrix methods. An experimental study of mirror-descent
reinforcement learning is presented using discrete and continuous Markov
decision processes.
|
1210.4894 | Heuristic Ranking in Tightly Coupled Probabilistic Description Logics | cs.AI cs.LO | The Semantic Web effort has steadily been gaining traction in the recent
years. In particular,Web search companies are recently realizing that their
products need to evolve towards having richer semantic search capabilities.
Description logics (DLs) have been adopted as the formal underpinnings for
Semantic Web languages used in describing ontologies. Reasoning under
uncertainty has recently taken a leading role in this arena, given the nature
of data found on theWeb. In this paper, we present a probabilistic extension of
the DL EL++ (which underlies the OWL2 EL profile) using Markov logic networks
(MLNs) as probabilistic semantics. This extension is tightly coupled, meaning
that probabilistic annotations in formulas can refer to objects in the
ontology. We show that, even though the tightly coupled nature of our language
means that many basic operations are data-intractable, we can leverage a
sublanguage of MLNs that allows to rank the atomic consequences of an ontology
relative to their probability values (called ranking queries) even when these
values are not fully computed. We present an anytime algorithm to answer
ranking queries, and provide an upper bound on the error that it incurs, as
well as a criterion to decide when results are guaranteed to be correct.
|
1210.4896 | Closed-Form Learning of Markov Networks from Dependency Networks | cs.LG cs.AI stat.ML | Markov networks (MNs) are a powerful way to compactly represent a joint
probability distribution, but most MN structure learning methods are very slow,
due to the high cost of evaluating candidates structures. Dependency networks
(DNs) represent a probability distribution as a set of conditional probability
distributions. DNs are very fast to learn, but the conditional distributions
may be inconsistent with each other and few inference algorithms support DNs.
In this paper, we present a closed-form method for converting a DN into an MN,
allowing us to enjoy both the efficiency of DN learning and the convenience of
the MN representation. When the DN is consistent, this conversion is exact. For
inconsistent DNs, we present averaging methods that significantly improve the
approximation. In experiments on 12 standard datasets, our methods are orders
of magnitude faster than and often more accurate than combining conditional
distributions using weight learning.
|
1210.4897 | Belief Propagation for Structured Decision Making | cs.AI | Variational inference algorithms such as belief propagation have had
tremendous impact on our ability to learn and use graphical models, and give
many insights for developing or understanding exact and approximate inference.
However, variational approaches have not been widely adoped for decision making
in graphical models, often formulated through influence diagrams and including
both centralized and decentralized (or multi-agent) decisions. In this work, we
present a general variational framework for solving structured cooperative
decision-making problems, use it to propose several belief propagation-like
algorithms, and analyze them both theoretically and empirically.
|
1210.4898 | Value Function Approximation in Noisy Environments Using Locally
Smoothed Regularized Approximate Linear Programs | cs.LG stat.ML | Recently, Petrik et al. demonstrated that L1Regularized Approximate Linear
Programming (RALP) could produce value functions and policies which compared
favorably to established linear value function approximation techniques like
LSPI. RALP's success primarily stems from the ability to solve the feature
selection and value function approximation steps simultaneously. RALP's
performance guarantees become looser if sampled next states are used. For very
noisy domains, RALP requires an accurate model rather than samples, which can
be unrealistic in some practical scenarios. In this paper, we demonstrate this
weakness, and then introduce Locally Smoothed L1-Regularized Approximate Linear
Programming (LS-RALP). We demonstrate that LS-RALP mitigates inaccuracies
stemming from noise even without an accurate model. We show that, given some
smoothness assumptions, as the number of samples increases, error from noise
approaches zero, and provide experimental examples of LS-RALP's success on
common reinforcement learning benchmark problems.
|
1210.4899 | Fast Exact Inference for Recursive Cardinality Models | cs.LG stat.ML | Cardinality potentials are a generally useful class of high order potential
that affect probabilities based on how many of D binary variables are active.
Maximum a posteriori (MAP) inference for cardinality potential models is
well-understood, with efficient computations taking O(DlogD) time. Yet
efficient marginalization and sampling have not been addressed as thoroughly in
the machine learning community. We show that there exists a simple algorithm
for computing marginal probabilities and drawing exact joint samples that runs
in O(Dlog2 D) time, and we show how to frame the algorithm as efficient belief
propagation in a low order tree-structured model that includes additional
auxiliary variables. We then develop a new, more general class of models,
termed Recursive Cardinality models, which take advantage of this efficiency.
Finally, we show how to do efficient exact inference in models composed of a
tree structure and a cardinality potential. We explore the expressive power of
Recursive Cardinality models and empirically demonstrate their utility.
|
1210.4900 | Probability and Asset Updating using Bayesian Networks for Combinatorial
Prediction Markets | cs.AI q-fin.TR | A market-maker-based prediction market lets forecasters aggregate information
by editing a consensus probability distribution either directly or by trading
securities that pay off contingent on an event of interest. Combinatorial
prediction markets allow trading on any event that can be specified as a
combination of a base set of events. However, explicitly representing the full
joint distribution is infeasible for markets with more than a few base events.
A factored representation such as a Bayesian network (BN) can achieve tractable
computation for problems with many related variables. Standard BN inference
algorithms, such as the junction tree algorithm, can be used to update a
representation of the entire joint distribution given a change to any local
conditional probability. However, in order to let traders reuse assets from
prior trades while never allowing assets to become negative, a BN based
prediction market also needs to update a representation of each user's assets
and find the conditional state in which a user has minimum assets. Users also
find it useful to see their expected assets given an edit outcome. We show how
to generalize the junction tree algorithm to perform all these computations.
|
1210.4901 | An Approximate Solution Method for Large Risk-Averse Markov Decision
Processes | q-fin.PM cs.AI cs.GT | Stochastic domains often involve risk-averse decision makers. While recent
work has focused on how to model risk in Markov decision processes using risk
measures, it has not addressed the problem of solving large risk-averse
formulations. In this paper, we propose and analyze a new method for solving
large risk-averse MDPs with hybrid continuous-discrete state spaces and
continuous action spaces. The proposed method iteratively improves a bound on
the value function using a linearity structure of the MDP. We demonstrate the
utility and properties of the method on a portfolio optimization problem.
|
1210.4902 | Efficiently Searching for Frustrated Cycles in MAP Inference | cs.DS cs.LG stat.ML | Dual decomposition provides a tractable framework for designing algorithms
for finding the most probable (MAP) configuration in graphical models. However,
for many real-world inference problems, the typical decomposition has a large
integrality gap, due to frustrated cycles. One way to tighten the relaxation is
to introduce additional constraints that explicitly enforce cycle consistency.
Earlier work showed that cluster-pursuit algorithms, which iteratively
introduce cycle and other higherorder consistency constraints, allows one to
exactly solve many hard inference problems. However, these algorithms
explicitly enumerate a candidate set of clusters, limiting them to triplets or
other short cycles. We solve the search problem for cycle constraints, giving a
nearly linear time algorithm for finding the most frustrated cycle of arbitrary
length. We show how to use this search algorithm together with the dual
decomposition framework and clusterpursuit. The new algorithm exactly solves
MAP inference problems arising from relational classification and stereo
vision.
|
1210.4903 | Detecting Change-Points in Time Series by Maximum Mean Discrepancy of
Ordinal Pattern Distributions | stat.ME cs.CE | As a new method for detecting change-points in high-resolution time series,
we apply Maximum Mean Discrepancy to the distributions of ordinal patterns in
different parts of a time series. The main advantage of this approach is its
computational simplicity and robustness with respect to (non-linear) monotonic
transformations, which makes it particularly well-suited for the analysis of
long biophysical time series where the exact calibration of measurement devices
is unknown or varies with time. We establish consistency of the method and
evaluate its performance in simulation studies. Furthermore, we demonstrate the
application to the analysis of electroencephalography (EEG) and
electrocardiography (ECG) recordings.
|
1210.4904 | Spectrum Identification using a Dynamic Bayesian Network Model of Tandem
Mass Spectra | cs.CE q-bio.QM | Shotgun proteomics is a high-throughput technology used to identify unknown
proteins in a complex mixture. At the heart of this process is a prediction
task, the spectrum identification problem, in which each fragmentation spectrum
produced by a shotgun proteomics experiment must be mapped to the peptide
(protein subsequence) which generated the spectrum. We propose a new algorithm
for spectrum identification, based on dynamic Bayesian networks, which
significantly outperforms the de-facto standard tools for this task: SEQUEST
and Mascot.
|
1210.4905 | Latent Composite Likelihood Learning for the Structured Canonical
Correlation Model | stat.ML cs.LG | Latent variable models are used to estimate variables of interest quantities
which are observable only up to some measurement error. In many studies, such
variables are known but not precisely quantifiable (such as "job satisfaction"
in social sciences and marketing, "analytical ability" in educational testing,
or "inflation" in economics). This leads to the development of measurement
instruments to record noisy indirect evidence for such unobserved variables
such as surveys, tests and price indexes. In such problems, there are
postulated latent variables and a given measurement model. At the same time,
other unantecipated latent variables can add further unmeasured confounding to
the observed variables. The problem is how to deal with unantecipated latents
variables. In this paper, we provide a method loosely inspired by canonical
correlation that makes use of background information concerning the "known"
latent variables. Given a partially specified structure, it provides a
structure learning approach to detect "unknown unknowns," the confounding
effect of potentially infinitely many other latent variables. This is done
without explicitly modeling such extra latent factors. Because of the special
structure of the problem, we are able to exploit a new variation of composite
likelihood fitting to efficiently learn this structure. Validation is provided
with experiments in synthetic data and the analysis of a large survey done with
a sample of over 100,000 staff members of the National Health Service of the
United Kingdom.
|
1210.4906 | Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing | cs.AI cs.DS | We consider the linear programming relaxation of an energy minimization
problem for Markov Random Fields. The dual objective of this problem can be
treated as a concave and unconstrained, but non-smooth function. The idea of
smoothing the objective prior to optimization was recently proposed in a series
of papers. Some of them suggested the idea to decrease the amount of smoothing
(so called temperature) while getting closer to the optimum. However, no
theoretical substantiation was provided. We propose an adaptive smoothing
diminishing algorithm based on the duality gap between relaxed primal and dual
objectives and demonstrate the efficiency of our approach with a smoothed
version of Sequential Tree-Reweighted Message Passing (TRW-S) algorithm. The
strategy is applicable to other algorithms as well, avoids adhoc tuning of the
smoothing during iterations, and provably guarantees convergence to the
optimum.
|
1210.4907 | From imprecise probability assessments to conditional probabilities with
quasi additive classes of conditioning events | cs.AI math.PR | In this paper, starting from a generalized coherent (i.e. avoiding uniform
loss) intervalvalued probability assessment on a finite family of conditional
events, we construct conditional probabilities with quasi additive classes of
conditioning events which are consistent with the given initial assessment.
Quasi additivity assures coherence for the obtained conditional probabilities.
In order to reach our goal we define a finite sequence of conditional
probabilities by exploiting some theoretical results on g-coherence. In
particular, we use solutions of a finite sequence of linear systems.
|
1210.4909 | Active Learning with Distributional Estimates | cs.LG stat.ML | Active Learning (AL) is increasingly important in a broad range of
applications. Two main AL principles to obtain accurate classification with few
labeled data are refinement of the current decision boundary and exploration of
poorly sampled regions. In this paper we derive a novel AL scheme that balances
these two principles in a natural way. In contrast to many AL strategies, which
are based on an estimated class conditional probability ^p(y|x), a key
component of our approach is to view this quantity as a random variable, hence
explicitly considering the uncertainty in its estimated value. Our main
contribution is a novel mathematical framework for uncertainty-based AL, and a
corresponding AL scheme, where the uncertainty in ^p(y|x) is modeled by a
second-order distribution. On the practical side, we show how to approximate
such second-order distributions for kernel density classification. Finally, we
find that over a large number of UCI, USPS and Caltech4 datasets, our AL scheme
achieves significantly better learning curves than popular AL methods such as
uncertainty sampling and error reduction sampling, when all use the same kernel
density classifier.
|
1210.4910 | New Advances and Theoretical Insights into EDML | cs.AI cs.LG stat.ML | EDML is a recently proposed algorithm for learning MAP parameters in Bayesian
networks. In this paper, we present a number of new advances and insights on
the EDML algorithm. First, we provide the multivalued extension of EDML,
originally proposed for Bayesian networks over binary variables. Next, we
identify a simplified characterization of EDML that further implies a simple
fixed-point algorithm for the convex optimization problem that underlies it.
This characterization further reveals a connection between EDML and EM: a fixed
point of EDML is a fixed point of EM, and vice versa. We thus identify also a
new characterization of EM fixed points, but in the semantics of EDML. Finally,
we propose a hybrid EDML/EM algorithm that takes advantage of the improved
empirical convergence behavior of EDML, while maintaining the monotonic
improvement property of EM.
|
1210.4911 | Multi-objective Influence Diagrams | cs.AI | We describe multi-objective influence diagrams, based on a set of p
objectives, where utility values are vectors in Rp, and are typically only
partially ordered. These can still be solved by a variable elimination
algorithm, leading to a set of maximal values of expected utility. If the
Pareto ordering is used this set can often be prohibitively large. We consider
approximate representations of the Pareto set based on e-coverings, allowing
much larger problems to be solved. In addition, we define a method for
incorporating user tradeoffs, which also greatly improves the efficiency.
|
1210.4912 | FHHOP: A Factored Hybrid Heuristic Online Planning Algorithm for Large
POMDPs | cs.AI | Planning in partially observable Markov decision processes (POMDPs) remains a
challenging topic in the artificial intelligence community, in spite of recent
impressive progress in approximation techniques. Previous research has
indicated that online planning approaches are promising in handling large-scale
POMDP domains efficiently as they make decisions "on demand" instead of
proactively for the entire state space. We present a Factored Hybrid Heuristic
Online Planning (FHHOP) algorithm for large POMDPs. FHHOP gets its power by
combining a novel hybrid heuristic search strategy with a recently developed
factored state representation. On several benchmark problems, FHHOP
substantially outperformed state-of-the-art online heuristic search approaches
in terms of both scalability and quality.
|
1210.4913 | An Improved Admissible Heuristic for Learning Optimal Bayesian Networks | cs.AI cs.LG stat.ML | Recently two search algorithms, A* and breadth-first branch and bound
(BFBnB), were developed based on a simple admissible heuristic for learning
Bayesian network structures that optimize a scoring function. The heuristic
represents a relaxation of the learning problem such that each variable chooses
optimal parents independently. As a result, the heuristic may contain many
directed cycles and result in a loose bound. This paper introduces an improved
admissible heuristic that tries to avoid directed cycles within small groups of
variables. A sparse representation is also introduced to store only the unique
optimal parent choices. Empirical results show that the new techniques
significantly improved the efficiency and scalability of A* and BFBnB on most
of datasets tested in this paper.
|
1210.4914 | Latent Structured Ranking | cs.LG cs.IR stat.ML | Many latent (factorized) models have been proposed for recommendation tasks
like collaborative filtering and for ranking tasks like document or image
retrieval and annotation. Common to all those methods is that during inference
the items are scored independently by their similarity to the query in the
latent embedding space. The structure of the ranked list (i.e. considering the
set of items returned as a whole) is not taken into account. This can be a
problem because the set of top predictions can be either too diverse (contain
results that contradict each other) or are not diverse enough. In this paper we
introduce a method for learning latent structured rankings that improves over
existing methods by providing the right blend of predictions at the top of the
ranked list. Particular emphasis is put on making this method scalable.
Empirical results on large scale image annotation and music recommendation
tasks show improvements over existing approaches.
|
1210.4916 | A Cluster-Cumulant Expansion at the Fixed Points of Belief Propagation | cs.AI | We introduce a new cluster-cumulant expansion (CCE) based on the fixed points
of iterative belief propagation (IBP). This expansion is similar in spirit to
the loop-series (LS) recently introduced in [1]. However, in contrast to the
latter, the CCE enjoys the following important qualities: 1) it is defined for
arbitrary state spaces 2) it is easily extended to fixed points of generalized
belief propagation (GBP), 3) disconnected groups of variables will not
contribute to the CCE and 4) the accuracy of the expansion empirically improves
upon that of the LS. The CCE is based on the same M\"obius transform as the
Kikuchi approximation, but unlike GBP does not require storing the beliefs of
the GBP-clusters nor does it suffer from convergence issues during belief
updating.
|
1210.4917 | Fast Graph Construction Using Auction Algorithm | cs.LG stat.ML | In practical machine learning systems, graph based data representation has
been widely used in various learning paradigms, ranging from unsupervised
clustering to supervised classification. Besides those applications with
natural graph or network structure data, such as social network analysis and
relational learning, many other applications often involve a critical step in
converting data vectors to an adjacency graph. In particular, a sparse subgraph
extracted from the original graph is often required due to both theoretic and
practical needs. Previous study clearly shows that the performance of different
learning algorithms, e.g., clustering and classification, benefits from such
sparse subgraphs with balanced node connectivity. However, the existing graph
construction methods are either computationally expensive or with
unsatisfactory performance. In this paper, we utilize a scalable method called
auction algorithm and its parallel extension to recover a sparse yet nearly
balanced subgraph with significantly reduced computational cost. Empirical
study and comparison with the state-ofart approaches clearly demonstrate the
superiority of the proposed method in both efficiency and accuracy.
|
1210.4918 | Dynamic Teaching in Sequential Decision Making Environments | cs.LG cs.AI stat.ML | We describe theoretical bounds and a practical algorithm for teaching a model
by demonstration in a sequential decision making environment. Unlike previous
efforts that have optimized learners that watch a teacher demonstrate a static
policy, we focus on the teacher as a decision maker who can dynamically choose
different policies to teach different parts of the environment. We develop
several teaching frameworks based on previously defined supervised protocols,
such as Teaching Dimension, extending them to handle noise and sequences of
inputs encountered in an MDP.We provide theoretical bounds on the learnability
of several important model classes in this setting and suggest a practical
algorithm for dynamic teaching.
|
1210.4919 | Latent Dirichlet Allocation Uncovers Spectral Characteristics of Drought
Stressed Plants | cs.LG cs.CE stat.ML | Understanding the adaptation process of plants to drought stress is essential
in improving management practices, breeding strategies as well as engineering
viable crops for a sustainable agriculture in the coming decades.
Hyper-spectral imaging provides a particularly promising approach to gain such
understanding since it allows to discover non-destructively spectral
characteristics of plants governed primarily by scattering and absorption
characteristics of the leaf internal structure and biochemical constituents.
Several drought stress indices have been derived using hyper-spectral imaging.
However, they are typically based on few hyper-spectral images only, rely on
interpretations of experts, and consider few wavelengths only. In this study,
we present the first data-driven approach to discovering spectral drought
stress indices, treating it as an unsupervised labeling problem at massive
scale. To make use of short range dependencies of spectral wavelengths, we
develop an online variational Bayes algorithm for latent Dirichlet allocation
with convolved Dirichlet regularizer. This approach scales to massive datasets
and, hence, provides a more objective complement to plant physiological
practices. The spectral topics found conform to plant physiological knowledge
and can be computed in a fraction of the time compared to existing LDA
approaches.
|
1210.4920 | Factorized Multi-Modal Topic Model | cs.LG cs.IR stat.ML | Multi-modal data collections, such as corpora of paired images and text
snippets, require analysis methods beyond single-view component and topic
models. For continuous observations the current dominant approach is based on
extensions of canonical correlation analysis, factorizing the variation into
components shared by the different modalities and those private to each of
them. For count data, multiple variants of topic models attempting to tie the
modalities together have been presented. All of these, however, lack the
ability to learn components private to one modality, and consequently will try
to force dependencies even between minimally correlating modalities. In this
work we combine the two approaches by presenting a novel HDP-based topic model
that automatically learns both shared and private topics. The model is shown to
be especially useful for querying the contents of one domain given samples of
the other.
|
1210.4981 | Foundations and Tools for End-User Architecting | cs.SE cs.HC cs.SI | Within an increasing number of domains an important emerging need is the
ability for technically naive users to compose computational elements into
novel configurations. Examples include astronomers who create new analysis
pipelines to process telescopic data, intelligence analysts who must process
diverse sources of unstructured text to discover socio-technical trends, and
medical researchers who have to process brain image data in new ways to
understand disease pathways. Creating such compositions today typically
requires low-level technical expertise, limiting the use of computational
methods and increasing the cost of using them. In this paper we describe an
approach - which we term end-user architecting - that exploits the similarity
between such compositional activities and those of software architects. Drawing
on the rich heritage of software architecture languages, methods, and tools, we
show how those techniques can be adapted to support end users in composing rich
computational systems through domain-specific compositional paradigms and
component repositories, without requiring that they have knowledge of the
low-level implementation details of the components or the compositional
infrastructure. Further, we outline a set of open research challenges that the
area of end-user architecting raises.
|
1210.5031 | Semi-Definite Programming Relaxation for Non-Line-of-Sight Localization | cs.IT cs.MA cs.NI math.IT | We consider the problem of estimating the locations of a set of points in a
k-dimensional euclidean space given a subset of the pairwise distance
measurements between the points. We focus on the case when some fraction of
these measurements can be arbitrarily corrupted by large additive noise. Given
that the problem is highly non-convex, we propose a simple semidefinite
programming relaxation that can be efficiently solved using standard
algorithms. We define a notion of non-contractibility and show that the
relaxation gives the exact point locations when the underlying graph is
non-contractible. The performance of the algorithm is evaluated on an
experimental data set obtained from a network of 44 nodes in an indoor
environment and is shown to be robust to non-line-of-sight errors.
|
1210.5034 | Optimal Computational Trade-Off of Inexact Proximal Methods | cs.LG cs.CV cs.NA | In this paper, we investigate the trade-off between convergence rate and
computational cost when minimizing a composite functional with
proximal-gradient methods, which are popular optimisation tools in machine
learning. We consider the case when the proximity operator is computed via an
iterative procedure, which provides an approximation of the exact proximity
operator. In that case, we obtain algorithms with two nested loops. We show
that the strategy that minimizes the computational cost to reach a solution
with a desired accuracy in finite time is to set the number of inner iterations
to a constant, which differs from the strategy indicated by a convergence rate
analysis. In the process, we also present a new procedure called SIP (that is
Speedy Inexact Proximal-gradient algorithm) that is both computationally
efficient and easy to implement. Our numerical experiments confirm the
theoretical findings and suggest that SIP can be a very competitive alternative
to the standard procedure.
|
1210.5035 | A Comparative Study of State Transition Algorithm with Harmony Search
and Artificial Bee Colony | math.OC cs.IT math.IT math.PR | We focus on a comparative study of three recently developed nature-inspired
optimization algorithms, including state transition algorithm, harmony search
and artificial bee colony. Their core mechanisms are introduced and their
similarities and differences are described. Then, a suit of 27 well-known
benchmark problems are used to investigate the performance of these algorithms
and finally we discuss their general applicability with respect to the
structure of optimization problems.
|
1210.5041 | Navigation domain representation for interactive multiview imaging | cs.MM cs.CV | Enabling users to interactively navigate through different viewpoints of a
static scene is a new interesting functionality in 3D streaming systems. While
it opens exciting perspectives towards rich multimedia applications, it
requires the design of novel representations and coding techniques in order to
solve the new challenges imposed by interactive navigation. Interactivity
clearly brings new design constraints: the encoder is unaware of the exact
decoding process, while the decoder has to reconstruct information from
incomplete subsets of data since the server can generally not transmit images
for all possible viewpoints due to resource constrains. In this paper, we
propose a novel multiview data representation that permits to satisfy bandwidth
and storage constraints in an interactive multiview streaming system. In
particular, we partition the multiview navigation domain into segments, each of
which is described by a reference image and some auxiliary information. The
auxiliary information enables the client to recreate any viewpoint in the
navigation segment via view synthesis. The decoder is then able to navigate
freely in the segment without further data request to the server; it requests
additional data only when it moves to a different segment. We discuss the
benefits of this novel representation in interactive navigation systems and
further propose a method to optimize the partitioning of the navigation domain
into independent segments, under bandwidth and storage constraints.
Experimental results confirm the potential of the proposed representation;
namely, our system leads to similar compression performance as classical
inter-view coding, while it provides the high level of flexibility that is
required for interactive streaming. Hence, our new framework represents a
promising solution for 3D data representation in novel interactive multimedia
services.
|
1210.5058 | Properties of Persistent Mutual Information and Emergence | math-ph cs.IT math.IT math.MP | The persistent mutual information (PMI) is a complexity measure for
stochastic processes. It is related to well-known complexity measures like
excess entropy or statistical complexity. Essentially it is a variation of the
excess entropy so that it can be interpreted as a specific measure of system
internal memory. The PMI was first introduced in 2010 by Ball, Diakonova and
MacKay as a measure for (strong) emergence. In this paper we define the PMI
mathematically and investigate the relation to excess entropy and statistical
complexity. In particular we prove that the excess entropy is an upper bound of
the PMI. Furthermore we show some properties of the PMI and calculate it
explicitly for some example processes. We also discuss to what extend it is a
measure for emergence and compare it with alternative approaches used to
formalize emergence.
|
1210.5117 | Distributed and Autonomous Resource and Power Allocation for Wireless
Networks | cs.IT cs.NI math.IT | In this paper, a distributed and autonomous technique for resource and power
allocation in orthogonal frequency division multiple access (OFDMA)
femto-cellular networks is presented. Here, resource blocks (RBs) and their
corresponding transmit powers are assigned to the user(s) in each cell
individually without explicit coordination between femto base stations (FBSs).
The "allocatability" of each resource is determined utilising only locally
available information of the following quantities: - the required rate of the
user; - the quality (i.e., strength) of the desired signal; - the
frequency-selective fading on each RB; and - the level of interference incident
on each RB. Using a fuzzy logic system, the time-averaged values of each of
these inputs are combined to determine which RBs are most suitable to be
allocated in a particular cell, i.e., which resources can be allocated such
that the user requested rate(s) in that cell are satisfied. Furthermore, link
adaptation (LA) is included, enabling users to adjust to varying channel
conditions. A comprehensive study of this system in a femto-cell environment is
performed, yielding system performance improvements in terms of throughput,
energy efficiency and coverage over state-of-the-art ICIC techniques.
|
1210.5118 | Creating a level playing field for all symbols in a discretization | cs.DS cs.AI | In time series analysis research there is a strong interest in discrete
representations of real valued data streams. One approach that emerged over a
decade ago and is still considered state-of-the-art is the Symbolic Aggregate
Approximation algorithm. This discretization algorithm was the first symbolic
approach that mapped a real-valued time series to a symbolic representation
that was guaranteed to lower-bound Euclidean distance. The interest of this
paper concerns the SAX assumption of data being highly Gaussian and the use of
the standard normal curve to choose partitions to discretize the data. Though
not necessarily, but generally, and certainly in its canonical form, the SAX
approach chooses partitions on the standard normal curve that would produce an
equal probability for each symbol in a finite alphabet to occur. This procedure
is generally valid as a time series is normalized before the rest of the SAX
algorithm is applied. However there exists a caveat to this assumption of
equi-probability due to the intermediate step of Piecewise Aggregate
Approximation (PAA). What we will show in this paper is that when PAA is
applied the distribution of the data is indeed altered, resulting in a
shrinking standard deviation that is proportional to the number of points used
to create a segment of the PAA representation and the degree of
auto-correlation within the series. Data that exhibits statistically
significant auto-correlation is less affected by this shrinking distribution.
As the standard deviation of the data contracts, the mean remains the same,
however the distribution is no longer standard normal and therefore the
partitions based on the standard normal curve are no longer valid for the
assumption of equal probability.
|
1210.5128 | A Novel Learning Algorithm for Bayesian Network and Its Efficient
Implementation on GPU | cs.DC cs.LG | Computational inference of causal relationships underlying complex networks,
such as gene-regulatory pathways, is NP-complete due to its combinatorial
nature when permuting all possible interactions. Markov chain Monte Carlo
(MCMC) has been introduced to sample only part of the combinations while still
guaranteeing convergence and traversability, which therefore becomes widely
used. However, MCMC is not able to perform efficiently enough for networks that
have more than 15~20 nodes because of the computational complexity. In this
paper, we use general purpose processor (GPP) and general purpose graphics
processing unit (GPGPU) to implement and accelerate a novel Bayesian network
learning algorithm. With a hash-table-based memory-saving strategy and a novel
task assigning strategy, we achieve a 10-fold acceleration per iteration than
using a serial GPP. Specially, we use a greedy method to search for the best
graph from a given order. We incorporate a prior component in the current
scoring function, which further facilitates the searching. Overall, we are able
to apply this system to networks with more than 60 nodes, allowing inferences
and modeling of bigger and more complex networks than current methods.
|
1210.5135 | LSBN: A Large-Scale Bayesian Structure Learning Framework for Model
Averaging | cs.LG stat.ML | The motivation for this paper is to apply Bayesian structure learning using
Model Averaging in large-scale networks. Currently, Bayesian model averaging
algorithm is applicable to networks with only tens of variables, restrained by
its super-exponential complexity. We present a novel framework, called
LSBN(Large-Scale Bayesian Network), making it possible to handle networks with
infinite size by following the principle of divide-and-conquer. The method of
LSBN comprises three steps. In general, LSBN first performs the partition by
using a second-order partition strategy, which achieves more robust results.
LSBN conducts sampling and structure learning within each overlapping community
after the community is isolated from other variables by Markov Blanket. Finally
LSBN employs an efficient algorithm, to merge structures of overlapping
communities into a whole. In comparison with other four state-of-art
large-scale network structure learning algorithms such as ARACNE, PC, Greedy
Search and MMHC, LSBN shows comparable results in five common benchmark
datasets, evaluated by precision, recall and f-score. What's more, LSBN makes
it possible to learn large-scale Bayesian structure by Model Averaging which
used to be intractable. In summary, LSBN provides an scalable and parallel
framework for the reconstruction of network structures. Besides, the complete
information of overlapping communities serves as the byproduct, which could be
used to mine meaningful clusters in biological networks, such as
protein-protein-interaction network or gene regulatory network, as well as in
social network.
|
1210.5161 | Predicting Group Evolution in the Social Network | cs.SI physics.soc-ph | Groups - social communities are important components of entire societies,
analysed by means of the social network concept. Their immanent feature is
continuous evolution over time. If we know how groups in the social network has
evolved we can use this information and try to predict the next step in the
given group evolution. In the paper, a new aproach for group evolution
prediction is presented and examined. Experimental studies on four evolving
social networks revealed that (i) the prediction based on the simple input
features may be very accurate, (ii) some classifiers are more precise than the
others and (iii) parameters of the group evolution extracion method
significantly influence the prediction quality.
|
1210.5167 | Influence of the Dynamic Social Network Timeframe Type and Size on the
Group Evolution Discovery | cs.SI physics.soc-ph | New technologies allow to store vast amount of data about users interaction.
From those data the social network can be created. Additionally, because
usually also time and dates of this activities are stored, the dynamic of such
network can be analysed by splitting it into many timeframes representing the
state of the network during specific period of time. One of the most
interesting issue is group evolution over time. To track group evolution the
GED method can be used. However, choice of the timeframe type and length might
have great influence on the method results. Therefore, in this paper, the
influence of timeframe type as well as timeframe length on the GED method
results is extensively analysed.
|
1210.5171 | Identification of Group Changes in Blogosphere | cs.SI physics.soc-ph | The paper addresses a problem of change identification in social group
evolution. A new SGCI method for discovering of stable groups was proposed and
compared with existing GED method. The experimental studies on a Polish
blogosphere service revealed that both methods are able to identify similar
evolution events even though both use different concepts. Some differences were
demonstrated as well
|
1210.5180 | Shortest Path Discovery in the Multi-layered Social Network | cs.SI physics.soc-ph | Multi-layered social networks consist of the fixed set of nodes linked by
multiple connections. These connections may be derived from different types of
user activities logged in the IT system. To calculate any structural measures
for multi-layered networks this multitude of relations should be coped with in
the parameterized way. Two separate algorithms for evaluation of shortest paths
in the multi-layered social network are proposed in the paper. The first one is
based on pre-processing - aggregation of multiple links into single
multi-layered edges, whereas in the second approach, many edges are processed
'on the fly' in the middle of path discovery. Experimental studies carried out
on the DBLP database converted into the multi-layered social network are
presented as well.
|
1210.5183 | LLR Compression for BICM Systems Using Large Constellations | cs.IT math.IT | Digital video broadcasting (DVB-C2) and other modern communication standards
increase diversity by means of a symbol-level interleaver that spans over
several codewords. De-interleaving at the receiver requires a large memory,
which has a significant impact on the implementation cost. In this paper, we
propose a technique that reduces the de-interleaver memory size. By quantizing
log-likelihood ratios with bit-specific quantizers and compressing the
quantized output, we can significantly reduce the memory size with a negligible
increase in computational complexity. Both the quantizer and compressor are
designed via a GMI-based maximization procedure. For a typical DVB-C2 scenario,
numerical results show that the proposed solution enables a memory saving up to
30%.
|
1210.5184 | A degree centrality in multi-layered social network | cs.SI physics.soc-ph | Multi-layered social networks reflect complex relationships existing in
modern interconnected IT systems. In such a network each pair of nodes may be
linked by many edges that correspond to different communication or
collaboration user activities. Multi-layered degree centrality for
multi-layered social networks is presented in the paper. Experimental studies
were carried out on data collected from the real Web 2.0 site. The
multi-layered social network extracted from this data consists of ten distinct
layers and the network analysis was performed for different degree centralities
measures.
|
1210.5196 | Matrix reconstruction with the local max norm | stat.ML cs.LG | We introduce a new family of matrix norms, the "local max" norms,
generalizing existing methods such as the max norm, the trace norm (nuclear
norm), and the weighted or smoothed weighted trace norms, which have been
extensively used in the literature as regularizers for matrix reconstruction
problems. We show that this new family can be used to interpolate between the
(weighted or unweighted) trace norm and the more conservative max norm. We test
this interpolation on simulated data and on the large-scale Netflix and
MovieLens ratings data, and find improved accuracy relative to the existing
matrix norms. We also provide theoretical results showing learning guarantees
for some of the new norms.
|
1210.5198 | Multiple Hypotheses Iterative Decoding of LDPC in the Presence of Strong
Phase Noise | cs.IT math.IT | Many satellite communication systems operating today employ low cost
upconverters or downconverters which create phase noise. This noise can
severely limit the information rate of the system and pose a serious challenge
for the detection systems. Moreover, simple solutions for phase noise tracking
such as PLL either require low phase noise or otherwise require many pilot
symbols which reduce the effective data rate. In the last decade we have
witnessed a significant amount of research done on joint estimation and
decoding of phase noise and coded information. These algorithms are based on
the factor graph representation of the joint posterior distribution. The
framework proposed in [5], allows the design of efficient message passing
algorithms which incorporate both the code graph and the channel graph. The use
of LDPC or Turbo decoders, as part of iterative message passing schemes, allows
the receiver to operate in low SNR regions while requiring less pilot symbols.
In this paper we propose a multiple hypotheses algorithm for joint detection
and estimation of coded information in a strong phase noise channel. We also
present a low complexity mixture reduction procedure which maintains very good
accuracy for the belief propagation messages.
|
1210.5215 | The scaling of human interactions with city size | physics.soc-ph cs.SI physics.data-an | The size of cities is known to play a fundamental role in social and economic
life. Yet, its relation to the structure of the underlying network of human
interactions has not been investigated empirically in detail. In this paper, we
map society-wide communication networks to the urban areas of two European
countries. We show that both the total number of contacts and the total
communication activity grow superlinearly with city population size, according
to well-defined scaling relations and resulting from a multiplicative increase
that affects most citizens. Perhaps surprisingly, however, the probability that
an individual's contacts are also connected with each other remains largely
unaffected. These empirical results predict a systematic and scale-invariant
acceleration of interaction-based spreading phenomena as cities get bigger,
which is numerically confirmed by applying epidemiological models to the
studied networks. Our findings should provide a microscopic basis towards
understanding the superlinear increase of different socioeconomic quantities
with city size, that applies to almost all urban systems and includes, for
instance, the creation of new inventions or the prevalence of certain
contagious diseases.
|
1210.5219 | The Domino Effect in Decentralized Wireless Networks | cs.IT cs.NI math.IT | Convergence of resource allocation algorithms is well covered in the
literature as convergence to a steady state is important due to stability and
performance. However, research is lacking when it comes to the propagation of
change that occur in a network due to new nodes arriving or old nodes leaving
or updating their allocation. As change can propagate through the network in a
manner similar to how domino pieces falls, we call this propagation of change
the domino effect. In this paper we investigate how change at one node can
affect other nodes for a simple power control algorithm. We provide analytical
results from a deterministic network as well as a Poisson distributed network
through percolation theory and provide simulation results that highlight some
aspects of the domino effect. The difficulty of mitigating this domino effect
lies in the fact that to avoid it, one needs to have a margin of tolerance for
changes in the network. However, a high margin leads to poor system performance
in a steady-state and therefore one has to consider a trade-off between
performance and propagation of change.
|
1210.5222 | Module Theorem for The General Theory of Stable Models | cs.AI cs.LO | The module theorem by Janhunen et al. demonstrates how to provide a modular
structure in answer set programming, where each module has a well-defined
input/output interface which can be used to establish the compositionality of
answer sets. The theorem is useful in the analysis of answer set programs, and
is a basis of incremental grounding and reactive answer set programming. We
extend the module theorem to the general theory of stable models by Ferraris et
al. The generalization applies to non-ground logic programs allowing useful
constructs in answer set programming, such as choice rules, the count
aggregate, and nested expressions. Our extension is based on relating the
module theorem to the symmetric splitting theorem by Ferraris et al. Based on
this result, we reformulate and extend the theory of incremental answer set
computation to a more general class of programs.
|
1210.5240 | Tracking Group Evolution in Social Networks | cs.SI physics.soc-ph | Easy access and vast amount of data, especially from long period of time,
allows to divide social network into timeframes and create temporal social
network. Such network enables to analyse its dynamics. One aspect of the
dynamics is analysis of social communities evolution, i.e., how particular
group changes over time. To do so, the complete group evolution history is
needed. That is why in this paper the new method for group evolution extraction
called GED is presented.
|
1210.5268 | Diffusion of Lexical Change in Social Media | cs.CL cs.SI physics.soc-ph | Computer-mediated communication is driving fundamental changes in the nature
of written language. We investigate these changes by statistical analysis of a
dataset comprising 107 million Twitter messages (authored by 2.7 million unique
user accounts). Using a latent vector autoregressive model to aggregate across
thousands of words, we identify high-level patterns in diffusion of linguistic
change over the United States. Our model is robust to unpredictable changes in
Twitter's sampling rate, and provides a probabilistic characterization of the
relationship of macro-scale linguistic influence to a set of demographic and
geographic predictors. The results of this analysis offer support for prior
arguments that focus on geographical proximity and population size. However,
demographic similarity -- especially with regard to race -- plays an even more
central role, as cities with similar racial demographics are far more likely to
share linguistic influence. Rather than moving towards a single unified
"netspeak" dialect, language evolution in computer-mediated communication
reproduces existing fault lines in spoken American English.
|
1210.5288 | A Scalable Null Model for Directed Graphs Matching All Degree
Distributions: In, Out, and Reciprocal | cs.SI physics.soc-ph | Degree distributions are arguably the most important property of real world
networks. The classic edge configuration model or Chung-Lu model can generate
an undirected graph with any desired degree distribution. This serves as a good
null model to compare algorithms or perform experimental studies. Furthermore,
there are scalable algorithms that implement these models and they are
invaluable in the study of graphs. However, networks in the real-world are
often directed, and have a significant proportion of reciprocal edges. A
stronger relation exists between two nodes when they each point to one another
(reciprocal edge) as compared to when only one points to the other (one-way
edge). Despite their importance, reciprocal edges have been disregarded by most
directed graph models.
We propose a null model for directed graphs inspired by the Chung-Lu model
that matches the in-, out-, and reciprocal-degree distributions of the real
graphs. Our algorithm is scalable and requires $O(m)$ random numbers to
generate a graph with $m$ edges. We perform a series of experiments on real
datasets and compare with existing graph models.
|
1210.5290 | A numerical framework for diffusion-controlled bimolecular-reactive
systems to enforce maximum principles and non-negative constraint | cs.NA cs.CE | We present a novel computational framework for diffusive-reactive systems
that satisfies the non-negative constraint and maximum principles on general
computational grids. The governing equations for the concentration of reactants
and product are written in terms of tensorial diffusion-reaction equations. %
We restrict our studies to fast irreversible bimolecular reactions. If one
assumes that the reaction is diffusion-limited and all chemical species have
the same diffusion coefficient, one can employ a linear transformation to
rewrite the governing equations in terms of invariants, which are unaffected by
the reaction. This results in two uncoupled tensorial diffusion equations in
terms of these invariants, which are solved using a novel non-negative solver
for tensorial diffusion-type equations. The concentrations of the reactants and
the product are then calculated from invariants using algebraic manipulations.
The novel aspect of the proposed computational framework is that it will always
produce physically meaningful non-negative values for the concentrations of all
chemical species. Several representative numerical examples are presented to
illustrate the robustness, convergence, and the numerical performance of the
proposed computational framework. We will also compare the proposed framework
with other popular formulations. In particular, we will show that the Galerkin
formulation (which is the standard single-field formulation) does not produce
reliable solutions, and the reason can be attributed to the fact that the
single-field formulation does not guarantee non-negative solutions. We will
also show that the clipping procedure (which produces non-negative solutions
but is considered as a variational crime) does not give accurate results when
compared with the proposed computational framework.
|
1210.5292 | Low-Complexity Demodulation for Interleaved OFDMA Downlink System Using
Circular Convolution | cs.IT math.IT | In this paper, a new low-complexity demodulation scheme is proposed for
interleaved orthogonal frequency division multiple access (OFDMA) downlink
system with N subcarriers and M users using circular convolution. In the
proposed scheme, each user's signal is extracted from the received interleaved
OFDMA signal of M users by using circular convolution in the time domain and
then fast Fourier transformed in the reduced size N over M. It is shown that
the computational complexity of the proposed scheme for the interleaved OFDMA
downlink system is much less than that of the conventional one.
|
1210.5297 | Adaptive Differential Feedback in Time-Varying Multiuser MIMO Channels | cs.IT math.IT | In the context of a time-varying multiuser multiple-input-multiple-output
(MIMO) system, we design recursive least squares based adaptive predictors and
differential quantizers to minimize the sum mean squared error of the overall
system. Using the fact that the scalar entries of the left singular matrix of a
Gaussian MIMO channel becomes almost Gaussian distributed even for a small
number of transmit antennas, we perform adaptive differential quantization of
the relevant singular matrix entries. Compared to the algorithms in the
existing differential feedback literature, our proposed quantizer provides
three advantages: first, the controller parameters are flexible enough to adapt
themselves to different vehicle speeds; second, the model is backward adaptive
i.e., the base station and receiver can agree upon the predictor and variance
estimator coefficients without explicit exchange of the parameters; third, it
can accurately model the system even when the correlation between two
successive channel samples becomes as low as 0.05. Our simulation results show
that our proposed method can reduce the required feedback by several kilobits
per second for vehicle speeds up to 20 km/h (channel tracker) and 10 km/h
(singular vector tracker). The proposed system also outperforms a fixed
quantizer, with same feedback overhead, in terms of bit error rate up to 30
km/h.
|
1210.5314 | Maximum Likelihood Algorithms for Joint Estimation of Synchronization
Impairments and Channel in MIMO-OFDM System | cs.IT math.IT | Maximum Likelihood (ML) algorithms, for the joint estimation of
synchronization impairments and channel in Multiple Input Multiple
Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system, are
investigated in this work. A system model that takes into account the effects
of carrier frequency offset, sampling frequency offset, symbol timing error,
and channel impulse response is formulated. Cram\'{e}r-Rao Lower Bounds for the
estimation of continuous parameters are derived, which show the coupling effect
among different impairments and the significance of the joint estimation. We
propose an ML algorithm for the estimation of synchronization impairments and
channel together, using grid search method. To reduce the complexity of the
joint grid search in ML algorithm, a Modified ML (MML) algorithm with multiple
one-dimensional searches is also proposed. Further, a Stage-wise ML (SML)
algorithm using existing algorithms, which estimate fewer number of parameters,
is also proposed. Performance of the estimation algorithms is studied through
numerical simulations and it is found that the proposed ML and MML algorithms
exhibit better performance than SML algorithm.
|
1210.5321 | The origin of Mayan languages from Formosan language group of
Austronesian | cs.CL q-bio.PE | Basic body-part names (BBPNs) were defined as body-part names in Swadesh
basic 200 words. Non-Mayan cognates of Mayan (MY) BBPNs were extensively
searched for, by comparing with non-MY vocabulary, including ca.1300 basic
words of 82 AN languages listed by Tryon (1985), etc. Thus found cognates (CGs)
in non-MY are listed in Table 1, as classified by language groups to which most
similar cognates (MSCs) of MY BBPNs belong. CGs of MY are classified to 23
mutually unrelated CG-items, of which 17.5 CG-items have their MSCs in
Austronesian (AN), giving its closest similarity score (CSS), CSS(AN) = 17.5,
which consists of 10.33 MSCs in Formosan, 1.83 MSCs in Western
Malayo-Polynesian (W.MP), 0.33 in Central MP, 0.0 in SHWNG, and 5.0 in Oceanic
[i.e., CSS(FORM)= 10.33, CSS(W.MP) = 1.88, ..., CSS(OC)= 5.0]. These CSSs for
language (sub)groups are also listed in the underline portion of every section
of (Section1 - Section 6) in Table 1. Chi-squar test (degree of freedom = 1)
using [Eq 1] and [Eqs.2] revealed that MSCs of MY BBPNs are distributed in
Formosan in significantly higher frequency (P < 0.001) than in other subgroups
of AN, as well as than in non-AN languages. MY is thus concluded to have been
derived from Formosan of AN. Eskimo shows some BBPN similarities to FORM and
MY.
|
1210.5323 | The performance of orthogonal multi-matching pursuit under RIP | cs.IT cs.LG math.IT math.NA | The orthogonal multi-matching pursuit (OMMP) is a natural extension of
orthogonal matching pursuit (OMP). We denote the OMMP with the parameter $M$ as
OMMP(M) where $M\geq 1$ is an integer. The main difference between OMP and
OMMP(M) is that OMMP(M) selects $M$ atoms per iteration, while OMP only adds
one atom to the optimal atom set. In this paper, we study the performance of
orthogonal multi-matching pursuit (OMMP) under RIP. In particular, we show
that, when the measurement matrix A satisfies $(9s, 1/10)$-RIP, there exists an
absolutely constant $M_0\leq 8$ so that OMMP(M_0) can recover $s$-sparse signal
within $s$ iterations. We furthermore prove that, for slowly-decaying
$s$-sparse signal, OMMP(M) can recover s-sparse signal within $O(\frac{s}{M})$
iterations for a large class of $M$. In particular, for $M=s^a$ with $a\in
[0,1/2]$, OMMP(M) can recover slowly-decaying $s$-sparse signal within
$O(s^{1-a})$ iterations. The result implies that OMMP can reduce the
computational complexity heavily.
|
1210.5338 | Pairwise MRF Calibration by Perturbation of the Bethe Reference Point | cond-mat.dis-nn cond-mat.stat-mech cs.LG stat.ML | We investigate different ways of generating approximate solutions to the
pairwise Markov random field (MRF) selection problem. We focus mainly on the
inverse Ising problem, but discuss also the somewhat related inverse Gaussian
problem because both types of MRF are suitable for inference tasks with the
belief propagation algorithm (BP) under certain conditions. Our approach
consists in to take a Bethe mean-field solution obtained with a maximum
spanning tree (MST) of pairwise mutual information, referred to as the
\emph{Bethe reference point}, for further perturbation procedures. We consider
three different ways following this idea: in the first one, we select and
calibrate iteratively the optimal links to be added starting from the Bethe
reference point; the second one is based on the observation that the natural
gradient can be computed analytically at the Bethe point; in the third one,
assuming no local field and using low temperature expansion we develop a dual
loop joint model based on a well chosen fundamental cycle basis. We indeed
identify a subclass of planar models, which we refer to as \emph{Bethe-dual
graph models}, having possibly many loops, but characterized by a singly
connected dual factor graph, for which the partition function and the linear
response can be computed exactly in respectively O(N) and $O(N^2)$ operations,
thanks to a dual weight propagation (DWP) message passing procedure that we set
up. When restricted to this subclass of models, the inverse Ising problem being
convex, becomes tractable at any temperature. Experimental tests on various
datasets with refined $L_0$ or $L_1$ regularization procedures indicate that
these approaches may be competitive and useful alternatives to existing ones.
|
1210.5374 | Timing Constraints Support on Petri-Net Model for Healthcare System
Design | cs.SE cs.SY | The worldwide healthcare organizations are facing a number of daunting
challenges forcing systems to benefit from modern technologies and telecom
capabilities. Hence, systems evolution through extension of the existing
information technology infrastructure becomes one of the most challenging
aspects of healthcare. In this paper, we present a newly architecture for
evolving healthcare systems towards a service-oriented architecture. Since
healthcare process exists in temporal context, timing constraints
satisfiability verification techniques are growing to enable designers to test
and repair design errors. Thanks to Hierarchical Timed Predicate Petri-Net
based conceptual framework, desirable properties such as deadlock free and safe
as well as timing constraints satisfiability can be easily checked by designer.
|
1210.5394 | Bayesian Estimation for Continuous-Time Sparse Stochastic Processes | cs.LG | We consider continuous-time sparse stochastic processes from which we have
only a finite number of noisy/noiseless samples. Our goal is to estimate the
noiseless samples (denoising) and the signal in-between (interpolation
problem).
By relying on tools from the theory of splines, we derive the joint a priori
distribution of the samples and show how this probability density function can
be factorized. The factorization enables us to tractably implement the maximum
a posteriori and minimum mean-square error (MMSE) criteria as two statistical
approaches for estimating the unknowns. We compare the derived statistical
methods with well-known techniques for the recovery of sparse signals, such as
the $\ell_1$ norm and Log ($\ell_1$-$\ell_0$ relaxation) regularization
methods. The simulation results show that, under certain conditions, the
performance of the regularization techniques can be very close to that of the
MMSE estimator.
|
1210.5403 | An Experience Report of Large Scale Federations | cs.DB | We present an experimental study of large-scale RDF federations on top of the
Bio2RDF data sources, involving 29 data sets with more than four billion RDF
triples deployed in a local federation. Our federation is driven by FedX, a
highly optimized federation mediator for Linked Data. We discuss design
decisions, technical aspects, and experiences made in setting up and optimizing
the Bio2RDF federation, and present an exhaustive experimental evaluation of
the federation scenario. In addition to a controlled setting with local
federation members, we study implications arising in a hybrid setting, where
local federation members interact with remote federation members exhibiting
higher network latency. The outcome demonstrates the feasibility of federated
semantic data management in general and indicates remaining bottlenecks and
research opportunities that shall serve as a guideline for future work in the
area of federated semantic data processing.
|
1210.5424 | Implementation of Distributed Time Exchange Based Cooperative Forwarding | cs.IT cs.NI math.IT | In this paper, we design and implement time exchange (TE) based cooperative
forwarding where nodes use transmission time slots as incentives for relaying.
We focus on distributed joint time slot exchange and relay selection in the sum
goodput maximization of the overall network. We formulate the design objective
as a mixed integer nonlinear programming (MINLP) problem and provide a
polynomial time distributed solution of the MINLP. We implement the designed
algorithm in the software defined radio enabled USRP nodes of the ORBIT indoor
wireless testbed. The ORBIT grid is used as a global control plane for exchange
of control information between the USRP nodes. Experimental results suggest
that TE can significantly increase the sum goodput of the network. We also
demonstrate the performance of a goodput optimization algorithm that is
proportionally fair.
|
1210.5454 | Stuck in Traffic (SiT) Attacks: A Framework for Identifying Stealthy
Attacks that Cause Traffic Congestion | cs.NI cs.MA | Recent advances in wireless technologies have enabled many new applications
in Intelligent Transportation Systems (ITS) such as collision avoidance,
cooperative driving, congestion avoidance, and traffic optimization. Due to the
vulnerable nature of wireless communication against interference and
intentional jamming, ITS face new challenges to ensure the reliability and the
safety of the overall system. In this paper, we expose a class of stealthy
attacks -- Stuck in Traffic (SiT) attacks -- that aim to cause congestion by
exploiting how drivers make decisions based on smart traffic signs. An attacker
mounting a SiT attack solves a Markov Decision Process problem to find
optimal/suboptimal attack policies in which he/she interferes with a
well-chosen subset of signals that are based on the state of the system. We
apply Approximate Policy Iteration (API) algorithms to derive potent attack
policies. We evaluate their performance on a number of systems and compare them
to other attack policies including random, myopic and DoS attack policies. The
generated policies, albeit suboptimal, are shown to significantly outperform
other attack policies as they maximize the expected cumulative reward from the
standpoint of the attacker.
|
1210.5470 | The DoF of Network MIMO with Backhaul Delays | cs.IT math.IT | We consider the problem of downlink precoding for Network (multi-cell) MIMO
networks where Transmitters (TXs) are provided with imperfect Channel State
Information (CSI). Specifically, each TX receives a delayed channel estimate
with the delay being specific to each channel component. This model is
particularly adapted to the scenarios where a user feeds back its CSI to its
serving base only as it is envisioned in future LTE networks. We analyze the
impact of the delay during the backhaul-based CSI exchange on the rate
performance achieved by Network MIMO. We highlight how delay can dramatically
degrade system performance if existing precoding methods are to be used. We
propose an alternative robust beamforming strategy which achieves the maximal
performance, in DoF sense. We verify by simulations that the theoretical DoF
improvement translates into a performance increase at finite Signal-to-Noise
Ratio (SNR) as well.
|
1210.5474 | Disentangling Factors of Variation via Generative Entangling | stat.ML cs.LG cs.NE | Here we propose a novel model family with the objective of learning to
disentangle the factors of variation in data. Our approach is based on the
spike-and-slab restricted Boltzmann machine which we generalize to include
higher-order interactions among multiple latent variables. Seen from a
generative perspective, the multiplicative interactions emulates the entangling
of factors of variation. Inference in the model can be seen as disentangling
these generative factors. Unlike previous attempts at disentangling latent
factors, the proposed model is trained using no supervised information
regarding the latent factors. We apply our model to the task of facial
expression classification.
|
1210.5486 | A Lightweight Stemmer for Gujarati | cs.CL | Gujarati is a resource poor language with almost no language processing tools
being available. In this paper we have shown an implementation of a rule based
stemmer of Gujarati. We have shown the creation of rules for stemming and the
richness in morphology that Gujarati possesses. We have also evaluated our
results by verifying it with a human expert.
|
1210.5500 | Modeling with Copulas and Vines in Estimation of Distribution Algorithms | cs.NE stat.ME | The aim of this work is studying the use of copulas and vines in the
optimization with Estimation of Distribution Algorithms (EDAs). Two EDAs are
built around the multivariate product and normal copulas, and other two are
based on pair-copula decomposition of vine models. Empirically we study the
effect of both marginal distributions and dependence structure separately, and
show that both aspects play a crucial role in the success of the optimization.
The results show that the use of copulas and vines opens new opportunities to a
more appropriate modeling of search distributions in EDAs.
|
1210.5502 | OpenCFU, a New Free and Open-Source Software to Count Cell Colonies and
Other Circular Objects | q-bio.QM cs.CV | Counting circular objects such as cell colonies is an important source of
information for biologists. Although this task is often time-consuming and
subjective, it is still predominantly performed manually. The aim of the
present work is to provide a new tool to enumerate circular objects from
digital pictures and video streams. Here, I demonstrate that the created
program, OpenCFU, is very robust, accurate and fast. In addition, it provides
control over the processing parameters and is implemented in an in- tuitive and
modern interface. OpenCFU is a cross-platform and open-source software freely
available at http://opencfu.sourceforge.net.
|
1210.5503 | Downlink Coordinated Multi-Point with Overhead Modeling in Heterogeneous
Cellular Networks | cs.IT math.IT | Coordinated multi-point (CoMP) communication is attractive for heterogeneous
cellular networks (HCNs) for interference reduction. However, previous
approaches to CoMP face two major hurdles in HCNs. First, they usually ignore
the inter-cell overhead messaging delay, although it results in an irreducible
performance bound. Second, they consider the grid or Wyner model for base
station locations, which is not appropriate for HCN BS locations which are
numerous and haphazard. Even for conventional macrocell networks without
overlaid small cells, SINR results are not tractable in the grid model nor
accurate in the Wyner model. To overcome these hurdles, we develop a novel
analytical framework which includes the impact of overhead delay for CoMP
evaluation in HCNs. This framework can be used for a class of CoMP schemes
without user data sharing. As an example, we apply it to downlink CoMP
zero-forcing beamforming (ZFBF), and see significant divergence from previous
work. For example, we show that CoMP ZFBF does not increase throughput when the
overhead channel delay is larger than 60% of the channel coherence time. We
also find that, in most cases, coordinating with only one other cell is nearly
optimum for downlink CoMP ZFBF.
|
1210.5515 | Quality of Service Support on High Level Petri-Net Based Model for
Dynamic Configuration of Web Service Composition | cs.SE cs.SY | Web services are widely used thanks to their features of universal
interoperability between software assets, platform independent and
loose-coupled. Web services composition is one of the most challenging topics
in service computing area. In this paper, an approach based on High Level
Petri-Net model as dynamic configuration schema of web services composition is
proposed to achieve self adaptation to run-time environment and self management
of composite web services. For composite service based applications, in
addition to functional requirements, quality of service properties should be
considered. This paper presents and proves some quality of service formulas in
context of web service composition. Based on this model and the quality of
service properties, a suitable configuration with optimal quality of service
can be selected in dynamic way to reach the goal of automatic service
composition. The correctness of the approach is proved by a simulation results
and corresponding analysis.
|
1210.5516 | Managing Changes in Citizen-Centric Healthcare Service Platform using
High Level Petri Net | cs.SE cs.SY | The healthcare organizations are facing a number of daunting challenges
pushing systems to deal with requirements changes and benefit from modern
technologies and telecom capabilities. Systems evolution through extension of
the existing information technology infrastructure becomes one of the most
challenging aspects of healthcare and the adaptation to changes is a must. The
paper presents a change management framework for a citizen-centric healthcare
service platform. A combination between Petri nets model to handle changes and
reconfigurable Petri nets model to react to these changes are introduced to
fulfill healthcare goals. Thanks to this management framework model,
consistency and correctness of a healthcare processes in the presence of
frequent changes can be checked and guaranteed.
|
1210.5517 | Design of English-Hindi Translation Memory for Efficient Translation | cs.CL | Developing parallel corpora is an important and a difficult activity for
Machine Translation. This requires manual annotation by Human Translators.
Translating same text again is a useless activity. There are tools available to
implement this for European Languages, but no such tool is available for Indian
Languages. In this paper we present a tool for Indian Languages which not only
provides automatic translations of the previously available translation but
also provides multiple translations, in cases where a sentence has multiple
translations, in ranked list of suggestive translations for a sentence.
Moreover this tool also lets translators have global and local saving options
of their work, so that they may share it with others, which further lightens
the task.
|
1210.5539 | Stability of Evolutionary Dynamics on Time Scales | math.DS cs.IT math.IT q-bio.PE | We combine incentive, adaptive, and time-scale dynamics to study
multipopulation dynamics on the simplex equipped with a large class of
Riemmanian metrics, simultaneously generalizing and extending many dynamics
commonly studied in dynamic game theory and evolutionary dynamics. Each
population has its own geometry, method of adaptation (incentive), and
time-scale (discrete, continuous, and others). Using an information-theoretic
measure of distance we give a widely-applicable Lyapunov result for the
dynamic. We include a wealth of examples leading up to and beyond the main
results.
|
1210.5544 | Online Learning in Decentralized Multiuser Resource Sharing Problems | cs.LG | In this paper, we consider the general scenario of resource sharing in a
decentralized system when the resource rewards/qualities are time-varying and
unknown to the users, and using the same resource by multiple users leads to
reduced quality due to resource sharing. Firstly, we consider a
user-independent reward model with no communication between the users, where a
user gets feedback about the congestion level in the resource it uses.
Secondly, we consider user-specific rewards and allow costly communication
between the users. The users have a cooperative goal of achieving the highest
system utility. There are multiple obstacles in achieving this goal such as the
decentralized nature of the system, unknown resource qualities, communication,
computation and switching costs. We propose distributed learning algorithms
with logarithmic regret with respect to the optimal allocation. Our logarithmic
regret result holds under both i.i.d. and Markovian reward models, as well as
under communication, computation and switching costs.
|
1210.5552 | Quickest Change Detection | math.ST cs.IT math.IT math.OC math.PR stat.AP stat.TH | The problem of detecting changes in the statistical properties of a
stochastic system and time series arises in various branches of science and
engineering. It has a wide spectrum of important applications ranging from
machine monitoring to biomedical signal processing. In all of these
applications the observations being monitored undergo a change in distribution
in response to a change or anomaly in the environment, and the goal is to
detect the change as quickly as possibly, subject to false alarm constraints.
In this chapter, two formulations of the quickest change detection problem,
Bayesian and minimax, are introduced, and optimal or asymptotically optimal
solutions to these formulations are discussed. Then some generalizations and
extensions of the quickest change detection problem are described. The chapter
is concluded with a discussion of applications and open issues.
|
1210.5560 | Wikipedia Vandalism Detection Through Machine Learning: Feature Review
and New Proposals: Lab Report for PAN at CLEF 2010 | cs.IR cs.AI | Wikipedia is an online encyclopedia that anyone can edit. In this open model,
some people edits with the intent of harming the integrity of Wikipedia. This
is known as vandalism. We extend the framework presented in (Potthast, Stein,
and Gerling, 2008) for Wikipedia vandalism detection. In this approach, several
vandalism indicating features are extracted from edits in a vandalism corpus
and are fed to a supervised learning algorithm. The best performing classifiers
were LogitBoost and Random Forest. Our classifier, a Random Forest, obtained an
AUC of 0.92236, ranking in the first place of the PAN'10 Wikipedia vandalism
detection task.
|
1210.5581 | Hidden Trends in 90 Years of Harvard Business Review | cs.CL cs.DL cs.IR | In this paper, we demonstrate and discuss results of our mining the abstracts
of the publications in Harvard Business Review between 1922 and 2012.
Techniques for computing n-grams, collocations, basic sentiment analysis, and
named-entity recognition were employed to uncover trends hidden in the
abstracts. We present findings about international relationships, sentiment in
HBR's abstracts, important international companies, influential technological
inventions, renown researchers in management theories, US presidents via
chronological analyses.
|
1210.5594 | Cross-Entropy Clustering | cs.IT math.IT | We construct a cross-entropy clustering (CEC) theory which finds the optimal
number of clusters by automatically removing groups which carry no information.
Moreover, our theory gives simple and efficient criterion to verify cluster
validity.
Although CEC can be build on an arbitrary family of densities, in the most
important case of Gaussian CEC:
{\em -- the division into clusters is affine invariant;
-- the clustering will have the tendency to divide the data into
ellipsoid-type shapes;
-- the approach is computationally efficient as we can apply Hartigan
approach.}
We study also with particular attention clustering based on the Spherical
Gaussian densities and that of Gaussian densities with covariance $s \I$. In
the letter case we show that with $s$ converging to zero we obtain the
classical k-means clustering.
|
1210.5626 | Compressed Sensing Signal Recovery via Forward-Backward Pursuit | cs.IT math.IT | Recovery of sparse signals from compressed measurements constitutes an l0
norm minimization problem, which is unpractical to solve. A number of sparse
recovery approaches have appeared in the literature, including l1 minimization
techniques, greedy pursuit algorithms, Bayesian methods and nonconvex
optimization techniques among others. This manuscript introduces a novel two
stage greedy approach, called the Forward-Backward Pursuit (FBP). FBP is an
iterative approach where each iteration consists of consecutive forward and
backward stages. The forward step first expands the support estimate by the
forward step size, while the following backward step shrinks it by the backward
step size. The forward step size is larger than the backward step size, hence
the initially empty support estimate is expanded at the end of each iteration.
Forward and backward steps are iterated until the residual power of the
observation vector falls below a threshold. This structure of FBP does not
necessitate the sparsity level to be known a priori in contrast to the Subspace
Pursuit or Compressive Sampling Matching Pursuit algorithms. FBP recovery
performance is demonstrated via simulations including recovery of random sparse
signals with different nonzero coefficient distributions in noisy and
noise-free scenarios in addition to the recovery of a sparse image.
|
1210.5631 | Content-boosted Matrix Factorization Techniques for Recommender Systems | stat.ML cs.LG | Many businesses are using recommender systems for marketing outreach.
Recommendation algorithms can be either based on content or driven by
collaborative filtering. We study different ways to incorporate content
information directly into the matrix factorization approach of collaborative
filtering. These content-boosted matrix factorization algorithms not only
improve recommendation accuracy, but also provide useful insights about the
contents, as well as make recommendations more easily interpretable.
|
1210.5644 | Efficient Inference in Fully Connected CRFs with Gaussian Edge
Potentials | cs.CV cs.AI cs.LG | Most state-of-the-art techniques for multi-class image segmentation and
labeling use conditional random fields defined over pixels or image regions.
While region-level models often feature dense pairwise connectivity,
pixel-level models are considerably larger and have only permitted sparse graph
structures. In this paper, we consider fully connected CRF models defined on
the complete set of pixels in an image. The resulting graphs have billions of
edges, making traditional inference algorithms impractical. Our main
contribution is a highly efficient approximate inference algorithm for fully
connected CRF models in which the pairwise edge potentials are defined by a
linear combination of Gaussian kernels. Our experiments demonstrate that dense
connectivity at the pixel level substantially improves segmentation and
labeling accuracy.
|
1210.5653 | Identifications of concealed weapon in a Human Body | cs.CV | The detection of weapons concealed underneath a person cloths is very much
important to the improvement of the security of the public as well as the
safety of public assets like airports, buildings and railway stations etc.
|
1210.5660 | Linear Physical-layer Network Coding in Galois Field for Rayleigh fading
2-Way Relay Channels | cs.IT math.IT | In this paper, we propose a novel linear physicallayer network coding (LPNC)
for Rayleigh fading 2-way relay channels (2-WRC). Rather than the simple
modulo-2 (bit-XOR) operation, the relay directly maps the superimposed signal
of the two users into the linear network coded combination in GF(2^2) by
multiplying the user data by properly selected generator matrix. We derive the
constellation constrained capacities for LPNC and 5QAM denoise-and forward
(5QAM-DNF) [2] and further explicitly characterize the capacity difference
between LPNC and 5QAM-DNF. Based on our analysis and simulation, we highlight
that without employing the irregular 5QAM mapping and sacrificing the spectral
efficiency, our LPNC in GF(2^2) is superior to 5QAM-DNF scheme in low SNR
regime while they achieve equal performance in the the moderate-to-high SNR
regime.
|
1210.5670 | Typed Answer Set Programming and Inverse Lambda Algorithms | cs.AI cs.LO cs.PL | Our broader goal is to automatically translate English sentences into
formulas in appropriate knowledge representation languages as a step towards
understanding and thus answering questions with respect to English text. Our
focus in this paper is on the language of Answer Set Programming (ASP). Our
approach to translate sentences to ASP rules is inspired by Montague's use of
lambda calculus formulas as meaning of words and phrases. With ASP as the
target language the meaning of words and phrases are ASP-lambda formulas. In an
earlier work we illustrated our approach by manually developing a dictionary of
words and their ASP-lambda formulas. However such an approach is not scalable.
In this paper our focus is on two algorithms that allow one to construct
ASP-lambda formulas in an inverse manner. In particular the two algorithms take
as input two lambda-calculus expressions G and H and compute a lambda-calculus
expression F such that F with input as G, denoted by F@G, is equal to H; and
similarly G@F = H. We present correctness and complexity results about these
algorithms. To do that we develop the notion of typed ASP-lambda calculus
theories and their orders and use it in developing the completeness results.
(To appear in Theory and Practice of Logic Programming.)
|
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